knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
The package is mainly designed for TCGA Data Analysis, currently including the 7 following steps:
getdata
automatically download the corresponding RNAseq count files(zipped).gdc-client can be download from Here!
r
setwd("yourworkdir")
currentwd=getwd()
#gdc-client.exe,gdc_manifest.txt in yourworkdir
getdata("gdc-client.exe","gdc_manifest.txt")
unzipData
unzip those downloaded zipped files
r
unzipData(currentwd)
generateMatrix
merge those seperate files into a gene expression matrix, whose rownames are genes and colnames are samples. Generally, this step would cost much resource to compelete
r
generateMatrix(currentwd)
metaAnnotation
to get the group information of samples, suchs as tumor, or paired normal
r
metaAnnotation(currentwd,"metadata.cart.xxxx.json")
corSample
calculate the correlation and PCA of samples based on the expression of all genes
r
corSamples(currentwd)
DEGsTCGA
apply DESeq2 to calculate the different expression genes(DEGs) between groups
r
DEGsTCGA(currentwd)
DEGsVisualization
according to the threshold of pvalue and log2foldchange, to visualize the DEGs by Heatmap, Volcano, etc.
r
DEGsVisualization(currentwd, log2FC=2, pvalue=0.05)
DEGsGeneSymbol
transfrom the ENSEMBLE ID to Gene Symbol
r
DEGsGeneSymbol(currentwd)
tcgaEnrichment
carry out the enrichment analysis, including GO and KEGG. Meanwhile, visualization of those results by dot and barplot
r
tcgaEnrichment(currentwd, pvalue=0.05)
tcgaClinic
combined with the clinic data of samples, to do survival analysis. Further, carry out kaplan-meier analysis on all DEGs
```r tcgaClinic(currentwd, "clinic.tsv", transfromed = T, groupby = 0.5)
```
Those are all steps included in this package. It's strongly recommended that you'd better do those steps one by one as previously mentioned. If you have any questions, please connect me (dongliulou@126.com) or follow me by wechat(yixuetongji001)
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